Ling 3.0 Flash VL
8450°
Run this model
Offers / Ads
inclusionAI | 131,072 token context | $0.06/M input, $0.18/M output. Ling 3.0 Flash VL builds on Ling 3.0 Flash (124B total / 5.5B active MoE from InclusionAI), further strengthening its language capabilities while adding native visual perception and advanced visual agent capabilities. Hybrid instant/reasoning model with tool calling.
Guides for this model
Best local LLM by use case (October 2026): the right open model for every job
How much VRAM do I actually need for local AI?
Open weights vs open source vs API-only: what you are actually allowed to do
What is a GGUF file? Quantized models explained (Q4, Q8, fp8, NF4)
Captain's notes
Notes from 2026-10-13
Ling 3.0 Flash VL is InclusionAI's open-weights multimodal model: a 124B-parameter sparse MoE that activates only 5.5B parameters per token, with native image and video understanding baked into its reasoning pipeline rather than bolted on. A 256K context window and hybrid instant/reasoning modes make it unusually capable for its inference cost. At $0.02 per million input tokens it suits developers who need vision and agentic workflows without a frontier-model bill.
Known for
- Native visual reasoning: vision feeds into planning and verification, not just description.
- Sparse MoE efficiency: 124B total parameters, only 5.5B active per token.
- 256K context window handles long documents, videos and extended agent histories.
- VideoRoPE encoding supports temporal understanding, event localization and long-video QA.
- Tool calling and visual agent capabilities for web and software interface tasks.
Watch out for
- 124B total parameters means local deployment requires serious hardware despite the low active parameter count.
- Thinking mode is on by default; you will need to account for that in latency-sensitive applications.
- The maker has not published detailed licence terms on this page — check the Hugging Face repository before commercial use.
Prompts to try
Analyse a chart or document
Attach an image of a chart, table or scanned document. Describe what it shows, identify any trends or anomalies, and extract the three most important figures as a structured list with a one-line explanation for each.
Visual agent: map a UI
Attach a screenshot of a web page or desktop application. List every interactive element you can see, describe what each one does, then write a step-by-step action sequence a script could follow to complete a login and navigate to account settings.
Multi-step visual reasoning
Attach an image containing a maths problem, a logic puzzle or a diagram with labelled parts. Work through the problem step by step using only the information visible in the image, show your reasoning at each step, and state your final answer clearly.
Compare with
- Ling 3.0 Flash SanteAPI-only model262,144 token context · $0.04/M input, $0.12/M output
- Ling 3.1 FlashAPI-only model262,144 token context · $0.07/M input, $0.22/M output
- Ling 3.0 FlashOpen-weights model262,144 token context · $0.02/M input, $0.06/M output
- Ling 3.0 Flash FinAPI-only model262,144 token context · $0.04/M input, $0.12/M output
More text models from inclusionai
Questions people ask
- Can Ling 3.0 Flash VL run locally?
- Yes, the weights are open and available on Hugging Face. Bear in mind the total parameter count is 124B, so local deployment demands substantial hardware even though only 5.5B parameters are active per token — check the VRAM guide before you commit.
- What does it cost and how long is the context?
- Via API it is priced at $0.02 per million input tokens and $0.06 per million output tokens, with a context window of 256K tokens.
- How does the VL version differ from Ling 3.0 Flash?
- Ling 3.0 Flash is a text-only model; the VL variant adds a ViT visual encoder and VideoRoPE temporal encoding, integrating image and video understanding into the same reasoning and agentic pipeline rather than treating vision as a separate module.
Captain's verdictA genuinely cheap entry point for vision-plus-agents work, and the open weights mean you are not locked to one API.
Model card on Hugging Face
Captain writes these notes from the model's own description and model card. Prices and context sizes are the provider's at the time of writing and can change.
Ranking badge
Updated Oct 22, 2026
Made this model? Show this badge on your page.
Paste it into your Hugging Face card or Civitai description.
Also ranked: #57 Hottest Text / LLM model · #57 Hottest Text / LLM text model
Not this creator?
Get your own badge
Ling 3.0 Flash VL Popularity Trend
Heat score since 10 Sep 2026
Heat score
8,450°
This week
▲
19.5%
8,426° then, 8,450° now
This month
▲
249%
8,284° then, 8,450° now
Past year
n/a
Wasn't released/tracked
7,000°
Floor
The scale runs from 0° to 9,999°. Everything below 7,000° is kept for models no source reports on yet, which carry a stand-in number there so lists stay complete. 7,000° is where measured data begins: a model with real data but almost no downloads sits at the floor, and the measurements fill the degrees above it. The floor says nothing about the model; only the degrees above it do, and they are what separates one model from another.
+1,450°
OpenRouter tokens
682,291,361,460 tokens this model processed through OpenRouter in the last 30 days, prompts and answers together. Every 10,000,000 tokens count as one download in the download term, 68,229 in all; the last day's tokens join the adoption rate. Source: OpenRouter (openrouter.ai/rankings).
Show detailed breakdown
Every number on this site can be checked. This model's heat score is 8,450°, and here it is built one ingredient at a time, with the running total after each: the floor, the degrees each source adds, the bonus for the kind of model and what the age of its weights takes away. The colors are the bands of the chart above. The glossary at the end defines every term.
- 1Start at the floor.Why 7,000° and not zero: the scale runs from 0° to 9,999°, and the part below 7,000° is reserved for models no source reports on yet, which carry a stand-in number there so the lists stay complete and never outrank a model with data. 7,000° is where measured data begins. A model with real data but almost no downloads sits here; everything that follows is measurement, and the room above the floor holds ten tenfold steps in downloads at 300° each. The floor is the same for every model, so it decides nothing; the degrees above it do.7,000°running total
- 2Add the downloads.68,229 download-equivalents in all, from OpenRouter tokens. Ten times the downloads adds the same 300°, so size matters but never runs away with the score.+1,450°8,450°running total
- =The heat score.Where this model stands on every list of this site. Nothing else adjusts it.8,450°
Glossary: the terms used here
- Heat score
- One number for how much a model is used and wanted right now, from 0° to 9,999°. Every list on this site that says hottest is sorted by it and nothing else.
- Floor
- The 7,000° every model with real data starts from. The scale runs from 0° to 9,999°; the part below the floor is reserved for stand-ins (models without data), so that a model with data always ranks above one without. The floor is the same for everyone and decides nothing; the degrees above it are the measurement.
- Download-equivalents
- Everything the score treats as a download, added up: downloads themselves, each torrent seeder as 1,000, each AI Horde image and each run on a generation site as one.
- Log scale
- Counting by tens, not by ones: going from 1,000 to 10,000 downloads adds the same degrees as going from 10,000 to 100,000. It keeps giants from crowding out everything else.
- Likes
- Thumbs up and likes from every source, worth half a download each on the log scale.
- Adoption rate
- How many downloads a day a model gets right now, from the difference between two readings of a source. A quarter of the weight of the download term.
- Measured and estimated
- Measured means two readings of a source exist and the difference is real. Estimated means only today's total is known and the past is spread over the model's life; the site says which one it is showing.
- Seeder
- Someone who keeps a copy of the weights online for others to download over a torrent, or a hosted mirror doing the same. Each one counts as 1,000 downloads.
- AI Horde
- A volunteer network that generates images for free on donated computers. The number of images it has made with a model is public, and each counts as one download.
- Generation sites
- Sites that host their own copies of a model and let people generate with it, such as PixAI, SeaArt and Yodayo. Their downloads count like Civitai's and each generation run as one download.
- OpenRouter tokens
- A text model served through an API, such as Claude or GPT, has no weights to download, so its usage is measured in tokens: the pieces of text it read and wrote through OpenRouter, a gateway many apps use, over the last 30 days. Every 10,000,000 tokens count as one download. Source: OpenRouter (openrouter.ai/rankings), CC BY 4.0.
- Full model (checkpoint)
- The weights themselves, the file that generates on its own. It gets a bonus because add-ons depend on it.
- LoRA and embedding
- Small add-on files that steer someone else's weights toward a style, a character or a concept. They cannot run alone.
- Family and weights
- The base a model is built on, such as SD 1.5, SDXL, Flux or Wan. Every model inherits the release date of its family's weights.
- Age
- Degrees taken off for how long ago the family's weights came out: 150° a year, up to 750°. It keeps old downloads that had years to pile up from freezing the top of the list.
- Family boost
- A small, bounded nudge for LoRAs of a family this site is pushing forward right now, always listed in the breakdown.
- Stand-in score
- A stable number below the floor given to a model no source reports on yet, so lists stay complete. It never ranks above a model with data.
- New! (grace period)
- A model less than 7 days old shows New! instead of a score and holds no place in the rankings. Its score is computed from day one, but a model one day old grows faster than anything, so without the wait the week's #1 would always be whatever came out yesterday rather than what held up for a week.
- Trend
- How a model's growth over a period compares with the typical model's growth over the same period. Faster than typical reads as a rise, slower as a fall.
- Typical model
- The middle of the catalog: half the models with a figure grew faster over the period, half slower.
How this model's popularity has moved against the rest of the catalog: its growth in downloads, likes and usage over each period, measured against the typical model's growth over the same period, with its heat score at the time. Above the typical growth reads as a rise, below it as a fall. The past is reconstructed from today's totals, the model's first day and its measured download speed where two readings exist; a model that did not exist at the start of a period shows n/a.
Ling 3.0 Flash VL is a text model for the Text / LLM family listed on ModelTrends.ai, a read-only catalog of open source AI image, video and text models. Compare it with other Text / LLM models and check its heat score to see how it is trending.
Browse by Model Family
Text / LLM Models
434
Anima Models
528
Chroma Models
19
Fibo Models
2
Flux Models
1,496
Flux 2 / Klein Models
1,239
Flux 3 Models
3
Moss Models
3
Iris Models
1
MiniMax H3 Models
364
Hunyuan Models
204
Ideogram Models
20
Krea2 Models
717
Qwen Models
20
Qwen2 Models
47
Ltx2 Models
153